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Learning to Learn in a Semi-supervised Fashion

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share disjoint ground truth label sets, which can be seen tasks like in person re-identification or image retrieval. Our learning scheme exploits the idea of leveraging information from labeled to unlabeled data. Instead of fitting the associated class-wise similarity scores as most meta-learning algorithms do, we propose to derive semantics-oriented similarity representations from labeled data, and transfer such representation to unlabeled ones. Thus, our strategy can be viewed as a self-supervised learning scheme, which can be applied to fully supervised learning tasks for improved performance. Our experiments on various tasks and settings confirm the effectiveness of our proposed approach and its superiority over the state-of-the-art methods.

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Notes

  1. 1.

    https://github.com/KovenYu/MAR.

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Acknowledgments

This paper is supported in part by the Ministry of Science and Technology (MOST) of Taiwan under grant MOST 109-2634-F-002-037.

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Authors and Affiliations

  1. Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan

    Yun-Chun Chen, Chao-Te Chou & Yu-Chiang Frank Wang

  2. ASUS Intelligent Cloud Services, Taipei, Taiwan

    Yu-Chiang Frank Wang

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  1. Yun-Chun Chen
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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Chen, YC., Chou, CT., Wang, YC.F. (2020). Learning to Learn in a Semi-supervised Fashion. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12363. Springer, Cham. https://doi.org/10.1007/978-3-030-58523-5_27

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